An accurate natural language detection library for React Native, powered by lingua-rs.

• 🎯 Highly Accurate: Uses advanced n-gram models (1-5) for superior detection accuracy
• 📦 75 Languages: Supports detection of 75 languages
• ⚡ Fast: Built with Rust and JSI for optimal performance
• 🔒 Type-Safe: Full TypeScript support
• 📱 Cross-Platform: Works on iOS and Android
• 🌐 Offline: No internet connection required

The native libraries are built automatically during installation. You'll need:

1. Rust toolchain:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

2. cargo-ndk (for Android):

cargo install cargo-ndk

3. Android NDK (for Android):

   • Recommended version: 26.1.10909125
   • Set environment variables:

   export ANDROID_SDK_ROOT=$HOME/Library/Android/sdk
   export ANDROID_HOME=$HOME/Library/Android/sdk
   export ANDROID_NDK_HOME=$ANDROID_HOME/ndk/26.1.10909125

4. Xcode (for iOS on macOS)

pnpm add react-native-lingua
# or
npm install react-native-lingua
# or
yarn add react-native-lingua

The native libraries will be built automatically during the native build process (Xcode/Gradle).

cd ios && pod install

No additional steps required.

If you need to rebuild the native libraries manually:

cd node_modules/react-native-lingua
npm run build:rust

import {
  createDetectorForAllLanguages,
  createDetectorForLanguages,
  detectLanguage,
} from 'react-native-lingua';

// Create a detector for all supported languages
const detector = createDetectorForAllLanguages();

// Detect language
const language = detectLanguage(detector, 'Hello, how are you?');
console.log(language); // 'en'

// Or create a detector for specific languages (faster and more accurate)
const specificDetector = createDetectorForLanguages(['en', 'fr', 'de', 'es']);
const lang = detectLanguage(specificDetector, 'Bonjour le monde');
console.log(lang); // 'fr'

Get confidence scores to understand how certain the detection is:

import {
  createDetectorForLanguages,
  computeLanguageConfidence,
  computeLanguageConfidenceValues,
} from 'react-native-lingua';

const detector = createDetectorForLanguages(['en', 'fr', 'de', 'es']);

// Get confidence for a specific language
const confidence = computeLanguageConfidence(detector, 'Hello world', 'en');
console.log(confidence); // 0.95

// Get confidence values for all languages
const confidences = computeLanguageConfidenceValues(detector, 'Hello world');

console.log(confidences);
// [
//   { language: 'en', confidence: 0.95 },
//   { language: 'fr', confidence: 0.03 },
//   { language: 'de', confidence: 0.01 },
//   { language: 'es', confidence: 0.01 }
// ]

The library supports 75 languages using ISO 639-1 codes. Some examples:

const commonLanguages = [
  'en', // English
  'es', // Spanish
  'fr', // French
  'de', // German
  'it', // Italian
  'pt', // Portuguese
  'ru', // Russian
  'ja', // Japanese
  'zh', // Chinese
  'ko', // Korean
  'ar', // Arabic
  'hi', // Hindi
  // ... and 63 more
];

For a complete list of supported languages, see the lingua-rs documentation.

Creates a language detector that can detect any of the 75 supported languages.

Returns: LanguageDetector

Note: Loading all languages consumes significant memory (~1 GB). Consider using createDetectorForLanguages() if you know which languages to expect.

Creates a language detector for specific languages.

Parameters:

• languages: string[] - Array of ISO 639-1 language codes

Returns: LanguageDetector

Example:

const detector = createDetectorForLanguages(['en', 'fr', 'de']);

Detects the language of the given text.

Parameters:

• detector: LanguageDetector - Language detector instance
• text: string - Text to analyze

Returns: string | null - ISO 639-1 language code or null if detection failed

Example:

const lang = detectLanguage(detector, 'Hello world'); // 'en'

Computes the confidence value for a specific language.

Parameters:

• detector: LanguageDetector - Language detector instance
• text: string - Text to analyze
• languageCode: string - ISO 639-1 language code

Returns: number - Confidence value between 0.0 and 1.0

Example:

const confidence = computeLanguageConfidence(detector, 'Hello world', 'en'); // 0.95

Computes confidence values for all languages in the detector.

Parameters:

• detector: LanguageDetector - Language detector instance
• text: string - Text to analyze

Returns: LanguageConfidence[] - Array of { language: string, confidence: number } sorted by confidence (descending)

Example:

const confidences = computeLanguageConfidenceValues(detector, 'Hello');
// [{ language: 'en', confidence: 0.95 }, ...]

1. Use specific languages: Create detectors with only the languages you need for better performance and accuracy
2. Reuse detectors: Create detector instances once and reuse them
3. Memory usage: The detector with all languages uses ~1 GB of memory. Use specific language sets to reduce memory footprint

This library wraps the Rust lingua-rs library using:

1. Rust FFI: C-compatible FFI bindings for the lingua library
2. C++ JSI: JavaScript Interface for direct communication with JavaScript
3. React Native TurboModule: Modern architecture for optimal performance

The detection uses both rule-based and statistical Naive Bayes methods with n-grams of sizes 1-5, providing high accuracy even for short text snippets.

See the contributing guide to learn how to contribute to the repository and the development workflow.

MIT

Made with create-react-native-library